Use and Design of Virtual Reality–Supported Learning Scenarios in the Vocational Qualification of Nursing Professionals: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Numerous reviews advocate using virtual reality (VR) in educational contexts. This medium allows learners to test experiences in realistic environments. Virtually supported scenarios offer a safe and motivating way to explore, practice, and consolidate nursing skills in rare and critical nursing tasks. This is also cited as one of the reasons why VR can significantly increase the knowledge acquisition of nursing students. Nevertheless, studies are limited in their significance owing to the chosen design. Despite great interest, this results in a low level of confidence in VR as a curricular teaching method for nursing education. Therefore, defining concrete design and didactic-methodological parameters that support teachers in the use and implementation of VR is more relevant. OBJECTIVE: This scoping review aims to provide an overview of significant design aspects for VR scenario conception and its transfer to generalist nursing education to generate value for the development of teaching scenarios and their sustainable implementation in teaching. METHODS: A comprehensive literature search was performed using the MEDLINE (via PubMed) and CINAHL databases, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist was applied. The search was conducted from May to July 2022, using a specific search principle corresponding to the focus and the growing study corpus. A previously defined "population, concept, and context" scheme was employed as the basis for the double-blind review of all relevant international German and English publications released up to May 1, 2022. RESULTS: In accordance with the predefined selection procedure, 22 publications were identified. The identified aspects aided in the development of design, didactic, and research recommendations. The intuitive operation of realistically designed VR scenarios, which are standardized, reliable, and modifiable, as well as clear instructions and specific multimodal feedback functions were described positively. The same applied to the linear structure of the sequences with graduated demands and high image quality for increased immersion with low sensory overload. Changes in perspectives, multiuser options, dialogs, and recording functions can contribute to an interactive care practice. On the research side, it is advisable to define VR terminologies. In addition to considering larger samples, varying settings, and financial issues, it is recommended to conduct long-term studies on knowledge acquisition or improved patient outcomes. CONCLUSIONS: VR scenarios offer high potential in the context of nursing education if teachers and learners develop them co-creatively according to design features and implement them by means of a well-conceived concept. VR enables trainees to develop practical skills continuously in a standardized way. In addition, its deployment supports the sensitization of trainees to digital nursing technologies and the expansion of their digital skills in a practical setting. Furthermore, it allows sustainability issues to be addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".